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arXiv · 1602.02845

Online Active Linear Regression via Thresholding

Carlos Riquelme·Ramesh Johari·Baosen Zhang
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Abstract

We consider the problem of online active learning to collect data for regression modeling. Specifically, we consider a decision maker with a limited experimentation budget who must efficiently learn an underlying linear population model. Our main contribution is a novel threshold-based algorithm for selection of most informative observations; we characterize its performance and fundamental lower bounds. We extend the algorithm and its guarantees to sparse linear regression in high-dimensional settings. Simulations suggest the algorithm is remarkably robust: it provides significant benefits over passive random sampling in real-world datasets that exhibit high nonlinearity and high dimensionality --- significantly reducing both the mean and variance of the squared error.

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Carlos Riquelme, Ramesh Johari, Baosen Zhang. 2016-02-09. Online Active Linear Regression via Thresholding. https://arxiv.org/abs/1602.02845

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